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Found 12 papers, 4 papers with code
COMET: Towards Partical W4A4KV4 LLMs Serving
We integrate the optimized W4Ax kernel into our inference framework, COMET, and provide efficient management to support popular LLMs such as LLaMA-3-70B.
Enhancing One-shot Pruned Pre-trained Language Models through Sparse-Dense-Sparse Mechanism
Next, we reconstruct a dense model featuring a pruning-friendly weight distribution by reactivating pruned connections with sparse regularization.
Mitral Regurgitation Recognition based on Unsupervised Out-of-Distribution Detection with Residual Diffusion Amplification
Since MR data is limited and has large intra-class variability, we propose an unsupervised out-of-distribution (OOD) detection method to identify MR rather than building a deep classifier.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Fine-grained Context and Multi-modal Alignment for Freehand 3D Ultrasound Reconstruction
In this context, we propose a novel method to exploit the long-range dependency management capabilities of the state space model (SSM) to address the above challenge.
Thyroid ultrasound diagnosis improvement via multi-view self-supervised learning and two-stage pre-training
In this study, we proposed a multi-view contrastive self-supervised method to improve thyroid nodule classification and segmentation performance with limited manual labels.
GarchingSim: An Autonomous Driving Simulator with Photorealistic Scenes and Minimalist Workflow
However, the availability of free and open-source simulators is limited, and the installation and configuration process can be daunting for beginners and interdisciplinary researchers.
A Foundation Model for General Moving Object Segmentation in Medical Images
In this paper, we propose the first foundation model, named iMOS, for MOS in medical images.
OnUVS: Online Feature Decoupling Framework for High-Fidelity Ultrasound Video Synthesis
Second, to better preserve the integrity and textural information of US images, we implemented a dual-decoder that decouples the content and textural features in the generator.
Instructive Feature Enhancement for Dichotomous Medical Image Segmentation
Deep neural networks have been widely applied in dichotomous medical image segmentation (DMIS) of many anatomical structures in several modalities, achieving promising performance.
Inverted Non-maximum Suppression for more Accurate and Neater Face Detection
Our method performs well on low-quality and tiny face samples.
Segment Anything Model for Medical Images?
To fully validate SAM's performance on medical data, we collected and sorted 53 open-source datasets and built a large medical segmentation dataset with 18 modalities, 84 objects, 125 object-modality paired targets, 1050K 2D images, and 6033K masks.
Domain Adaptive Cascade R-CNN for MItosis DOmain Generalization (MIDOG) Challenge
We present a summary of the domain adaptive cascade R-CNN method for mitosis detection of digital histopathology images.
